1. Managing Sustainability Solutions Initiative (SSI) data Kate Beard, Steve Cousins University of Maine NERACOOS/NECOSP Data Management Workshop, Sept. 26, 2012

2. SSI data management project objectives and research approach Identify data needs and types of data used within the portfolio of SSI projects Develop framework for data search, access, archiving Provide access to computational resources Support data documentation and curation Provide a foundation for linking knowledge and data through ontologies Enhance and complement the research portfolio Team: Beard, Chawathe, Colgan, Jain, McGill, and Segee (University of Maine, University of Southern Maine)

3. Activities: SSI Data Inventory Catalog the breadth and depth of SSI data holdings – Thematic variables: urbanization, forestry, climate change – Spatial and temporal scales – Data type Identify data management tools and protocols – Metadata development – Data access and sharing within teams and among stakeholders – Archiving and storage of data – Restrictions on data access and sharing (IRB)

4. Data Survey Findings Data types collected include – Geospatial- GIS layers and imagery – Surveys and survey data (social surveys) – Model runs – Time series - geochemical, hydrological, land use observations Data management methods and needs varied broadly across teams, but some common themes were: – Metadata standards for non-GIS datasets are largely unknown – Limited time and expertise for data documentation – Graduate students as keepers of data-poses challenges to continuity – Use of cloud computing and access to a common server is not well understood

5. Project Data Server Housed at UM Accessible statewide Redmine Front End Dspace: Data Storage and Metadata management for Communities GeoServer-Post-GIS For Handling Spatial Data

6. Data Management Successes DSpace has worked as a general repository although limited in search capability. Data (e.g. tax parcel information) is shared and made generally accessible to all project teams through DSpace.

7. Challenges and Opportunities Share data and economize in data collection and reuse Contextualize stakeholder surveys Link stakeholder views within and across project findings Link findings and develop insights across projects

8. Summary points So far we have not been able to identify any repository framework that can handle the level of data diversity represented in SSI. Metadata resolves to least common denominator: Dublin Core

9. Challenges of this scientific generation: how to develop, implement and support the new methods, management structures and technologies to store and manage the diversity, size, and complexity of current and future data sets and data streams. NSF- DIBBS: Data infrastructure Building Blocks Planning awards aimed at further developing disciplinary and interdisciplinary communities' understanding of their data storage and management requirements with the goal of developing an initial prototype. Any activity that brings the community together to address common problems, further refine requirements and avoid unnecessary and wasteful duplication of resources and efforts will be eligible for funding.